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Hal R. Varian
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253Publications
58H-index
27.8kCitations
Publications 253
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Machine learning (ML) and artificial intelligence (AI) have been around for many years. However, in the last 5 years, remarkable progress has been made using multilayered neural networks in diverse areas such as image recognition, speech recognition, and machine translation. AI is a general purpose technology that is likely to impact many industries. In this chapter I consider how machine learning availability might affect the industrial organization of both firms that provide AI services and in...
#1Seth I. Stephens-Davidowitz (UPenn: University of Pennsylvania)H-Index: 4
#2Hal R. Varian (University of California, Berkeley)H-Index: 58
Last.Michael D. Smith (CMU: Carnegie Mellon University)H-Index: 48
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This paper uses a natural experiment—the Super Bowl—to study the causal effect of advertising on demand for movies. Identification of the causal effect rests on two points: 1) Super Bowl ads are purchased before advertisers know which teams will play; 2) home cities of the teams that are playing will have proportionally more viewers than viewers in other cities. We find that the movies in our sample experience on average incremental opening weekend ticket sales of about 8.4 million from a mi...
This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual—a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference.
#1Hal R. Varian (University of California, Berkeley)H-Index: 58
This lecture provides an introduction to the economics of Internet search engines. After a brief review of the historical development of the technology and the industry, I describe some of the economic features of the auction system used for displaying ads. It turns out that some relatively simple economic models provide significant insight into the operation of these auctions. In particular, the classical theory of two-sided matching markets turns out to be very useful in this context.
#1Hal R. VarianH-Index: 58
This is a short and very elementary introduction to causal inference in social science applications targeted to machine learners. I illustrate the techniques described with examples chosen from the economics and marketing literature.
#1Hal R. VarianH-Index: 58
#2Esther RabascoH-Index: 1
Durante mas de veinticinco anos Microeconomia intermedia de Hal R. Varian ha ofrecido a los estudiantes el texto mas actual y completo de microeconomia intermedia. El texto del profesor Varian ensena a los estudiantes a conocer los fundamentos del analisis de los temas mas innovadores. La novena edicion contiene casos practicos y ejemplos contemporaneos y cubre la crisis economica actual. Incluye un nuevo capitulo que describe el uso de datos observacionales y experimentales en la estimacion de ...
We describe two auction forms for search engine advertising and present two simple theoretical results concerning i) the estimation of click-through rates and ii) how to adjust the auctions for broad match search. We also describe some of the practical issues involved in implementing a VCG auction.
#1Hal R. Varian (University of California, Berkeley)H-Index: 58
Computers are now involved in many economic transactions and can capture data associated with these transactions, which can then be manipulated and analyzed. Conventional statistical and econometric techniques such as regression often work well, but there are issues unique to big datasets that may require different tools. First, the sheer size of the data involved may require more powerful data manipulation tools. Second, we may have more potential predictors than appropriate for estimation, so ...
This article describes a system for short term forecasting based on an ensemble prediction that averages over different combinations of predictors. The system combines a structural time series model for the target series with regression component capturing the contributions of contemporaneous search query data. A spike-and-slab prior on the regression coefficients induces sparsity, dramatically reducing the size of the regression problem. Our system averages over potential contributions from a v...
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